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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from handler import EndpointHandler\n",
"import base64\n",
"from io import BytesIO\n",
"from PIL import Image\n",
"import cv2\n",
"import random\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# helper decoder\n",
"def decode_base64_image(image_string):\n",
" base64_image = base64.b64decode(image_string)\n",
" buffer = BytesIO(base64_image)\n",
" return Image.open(buffer)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# init handler\n",
"my_handler = EndpointHandler(path=\".\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image.size: (1200, 517), image.mode: RGBA, outscale: 10.0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"output.shape: (5170, 12000, 4)\n",
"out_image.size: (12000, 5170)\n",
"image.size: (1056, 1068), image.mode: RGB, outscale: 3.0\n",
"output.shape: (3204, 3168, 3)\n",
"out_image.size: (3168, 3204)\n",
"image.size: (1056, 1068), image.mode: L, outscale: 5.49\n",
"output.shape: (5863, 5797, 3)\n",
"out_image.size: (5797, 5863)\n"
]
}
],
"source": [
"img_dir = \"test_data/\"\n",
"img_names = [\"4121783.png\", \"FB_IMG_1725931665635.jpg\", \"FB_IMG_1725931665635_gray.jpg\"]\n",
"out_scales = [10, 3, 5.49]\n",
"for img_name, outscale in zip(img_names, out_scales):\n",
" image_path = img_dir + img_name\n",
" # create payload\n",
" with open(image_path, \"rb\") as i:\n",
" b64 = base64.b64encode(i.read())\n",
" b64 = b64.decode(\"utf-8\")\n",
" payload = {\n",
" \"inputs\": {\"image\": b64, \n",
" \"outscale\": outscale\n",
" }\n",
" }\n",
"\n",
"\n",
" output_payload = my_handler(payload)\n",
" out_image = decode_base64_image(output_payload[\"out_image\"])\n",
" print(f\"out_image.size: {out_image.size}\")\n",
" out_image.save(f\"test_data/outputs/{img_name.split('.')[0]}_outscale_{outscale}.png\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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